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"""
Python Backend API to chat with private data
08/16/2023
D.M. Theekshana Samaradiwakara
"""
import os
import time
import streamlit as st
from streamlit.logger import get_logger
logger = get_logger(__name__)
from ui.htmlTemplates import css, bot_template, user_template, source_template
from config import MODELS, DATASETS
from qaPipeline import QAPipeline
from faissDb import create_faiss
# loads environment variables
from dotenv import load_dotenv
load_dotenv()
isHuggingFaceHubEnabled = os.environ.get('ENABLE_HUGGINGFSCE_HUB_MODELS')
isOpenAiApiEnabled = os.environ.get('ENABLE_OPENAI_API_MODELS')
st.set_page_config(page_title="Chat with data",
page_icon=":books:")
st.write(css, unsafe_allow_html=True)
SESSION_DEFAULTS = {
"model": MODELS["DEFAULT"],
"dataset": DATASETS["DEFAULT"],
"chat_history": None,
"is_parameters_changed":False,
"show_source_files": False,
"user_question":'',
}
for k, v in SESSION_DEFAULTS.items():
if k not in st.session_state:
st.session_state[k] = v
with st.sidebar:
st.subheader("Chat parameters")
with st.form('param_form'):
chat_model = st.selectbox(
"Chat model",
MODELS,
key="chat_model",
help="Select the LLM model for the chat",
# on_change=update_parameters_change,
)
st.session_state.dataset = "DEFAULT"
show_source = st.checkbox(
label="show source files",
key="show_source",
help="Select this to show relavant source files for the query",
)
submitted = st.form_submit_button(
"Submit",
# on_click=parameters_change_button,
# args=[chat_model, show_source]
)
# submitted = st.button(
# "Submit",
# # on_click=parameters_change_button,
# # args=[chat_model, show_source]
# )
if submitted:
st.session_state.model = chat_model
st.session_state.dataset = "DEFAULT"
st.session_state.show_source_files = show_source
st.session_state.is_parameters_changed = False
alert = st.success("chat parameters updated")
time.sleep(1) # Wait for 3 seconds
alert.empty() # Clear the alert
st.markdown("\n")
# if st.button("Create FAISS db"):
# try:
# with st.spinner('creating faiss vector store'):
# create_faiss()
# st.success('faiss saved')
# except Exception as e:
# st.error(f"Error : {e}")#, icon=":books:")
# return
st.markdown(
"### How to use\n"
"1. Select the chat model\n" # noqa: E501
"2. Select \"show source files\" to show the source files related to the answer.📄\n"
"3. Ask a question about the documents💬\n"
)
st.header("Chat with your own data:")
@st.experimental_singleton # 👈 Add the caching decorator
def load_QaPipeline():
print('> QAPipeline loaded for front end')
return QAPipeline()
qaPipeline = load_QaPipeline()
# qaPipeline = QAPipeline()
with st.form('chat_body'):
user_question = st.text_input(
"Ask a question about your documents:",
placeholder="enter question",
key='user_question',
# on_change=submit_user_question,
)
submitted = st.form_submit_button(
"Submit",
# on_click=submit_user_question
)
if submitted:
with st.spinner("Processing"):
user_question = st.session_state.user_question
# st.success(user_question)
query = user_question
# st.session_state.user_question=''
# Get the answer from the chain
try:
if (not query) or (query.strip() == ''):
st.error("Please enter a question!")
st.stop()
model = MODELS[st.session_state.model]
dataset = DATASETS[st.session_state.dataset]
show_source_files = st.session_state.show_source_files
# Try to access openai and deeplake
print(f">\n model: {model} \n dataset : {dataset} \n show_source_files : {show_source_files}")
# response = qaPipeline.run(query=query, model=model, dataset=dataset)
response = qaPipeline.run_agent(query=query, model=model, dataset=dataset)
docs = []
if isinstance(response, dict):
answer, docs = response['answer'], response['source_documents']
else:
answer = response
st.write(user_template.replace(
"{{MSG}}", query), unsafe_allow_html=True)
st.write(bot_template.replace(
"{{MSG}}", answer ), unsafe_allow_html=True)
if show_source_files:
# st.write(source_template.replace(
# "{{MSG}}", "source files" ), unsafe_allow_html=True)
if len(docs)>0 :
st.markdown("#### source files : ")
for source in docs:
# st.info(source.metadata)
with st.expander(source.metadata["source"]):
st.markdown(source.page_content)
# st.write(response)
except Exception as e:
# logger.error(f"Answer retrieval failed with {e}")
st.error(f"Error : {e}")#, icon=":books:")
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